Confidence S. is an accomplished data engineer with expertise in Python, SQL, and Spark, and a proven track record designing ELT and ETL pipelines across cloud platforms such as GCP (BigQuery, Cloud Storage, Dataflow), AWS (Redshift, S3, Glue, Lambda, EMR), and Azure (Synapse Analytics, Databricks, ADF, ADLS Gen2), as well as Microsoft Fabric. His technical proficiency extends to technologies like Apache Spark, Apache Kafka, RabbitMQ, Apache Airflow, DBT, Azure Data Factory, SSIS, Databricks Workflows, and Medallion Architecture using ELT/ETL frameworks. Confidence has designed and implemented modern data warehouses, built real-time fraud detection and multimodal ingestion pipelines, and automated testing and deployment workflows using Git, Docker, and GitHub Actions for robust CI/CD. He is highly skilled in data modeling with PostgreSQL, SQL Server, Oracle, and MongoDB, and has hands-on experience with data quality initiatives, Power BI, and advanced API development. His successful projects include large-scale identity fraud detection pipelines using Kafka, PySpark, and Delta Lake on Databricks, migration of enterprise data warehouses to BigQuery, and developing fully automated data pipelines for LLM training datasets. With certifications in Google Cloud Professional Data Engineering, Microsoft Fabric Data Engineering, Power BI, Azure Administration, and SQL Database Administration, Confidence brings a deep understanding of data engineering best practices, data quality, and efficient cloud-based analytics to any team.